AirLLM

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   AirLLM claims 2.8T-parameter Kimi K3 runs on a single 4GB GPU, adds low-VRAM training

   AirLLM is an open-source inference and training library that streams weights one layer at a time, keeping only a single layer resident on the GPU to cut VRAM use sharply. According to its repository, it runs 70B models on a single 4GB card without quantization, distillation or pruning, and reports running Kimi K3 (2.8T) in 3.72GB, Qwen3.8-Flash-Next (125B) in about 5.95GB and DeepSeek-V3 (671B) in roughly 12GB. Recent updates also add small-VRAM training, fine-tuning Qwen3.8-Flash-Next (125B) under 6GB and Qwen3.8-27B at about 2GB. All VRAM and training figures are the project's own reported measurements and have not been independently verified.

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